Overview

Dataset statistics

Number of variables23
Number of observations2938
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory528.0 KiB
Average record size in memory184.0 B

Variable types

Numeric21
Categorical2

Alerts

Country has a high cardinality: 193 distinct valuesHigh cardinality
Life_Expectancy is highly overall correlated with Adult_Mortality and 12 other fieldsHigh correlation
Adult_Mortality is highly overall correlated with Life_Expectancy and 2 other fieldsHigh correlation
Infant_Deaths is highly overall correlated with Life_Expectancy and 4 other fieldsHigh correlation
Alcohol is highly overall correlated with Education and 1 other fieldsHigh correlation
Percent_Expenditure is highly overall correlated with GDPHigh correlation
HepB is highly overall correlated with Polio and 1 other fieldsHigh correlation
Measles is highly overall correlated with Infant_Deaths and 1 other fieldsHigh correlation
BMI is highly overall correlated with Life_Expectancy and 6 other fieldsHigh correlation
lt5_Deaths is highly overall correlated with Life_Expectancy and 6 other fieldsHigh correlation
Polio is highly overall correlated with Life_Expectancy and 4 other fieldsHigh correlation
Diphtheria is highly overall correlated with Life_Expectancy and 4 other fieldsHigh correlation
HIV/AIDS is highly overall correlated with Life_Expectancy and 5 other fieldsHigh correlation
GDP is highly overall correlated with Life_Expectancy and 3 other fieldsHigh correlation
Thinness_1-19y is highly overall correlated with Life_Expectancy and 4 other fieldsHigh correlation
Thinness_5-9y is highly overall correlated with Life_Expectancy and 4 other fieldsHigh correlation
Income_Composition is highly overall correlated with Life_Expectancy and 12 other fieldsHigh correlation
Education is highly overall correlated with Life_Expectancy and 12 other fieldsHigh correlation
Dev_Status is highly overall correlated with Life_Expectancy and 3 other fieldsHigh correlation
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
Infant_Deaths has 848 (28.9%) zerosZeros
Percent_Expenditure has 611 (20.8%) zerosZeros
Measles has 983 (33.5%) zerosZeros
lt5_Deaths has 785 (26.7%) zerosZeros
Income_Composition has 133 (4.5%) zerosZeros

Reproduction

Analysis started2023-01-06 05:05:08.989097
Analysis finished2023-01-06 05:05:56.882348
Duration47.89 seconds
Software versionpandas-profiling vdev
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct2938
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1468.5
Minimum0
Maximum2937
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:05:56.982301image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile146.85
Q1734.25
median1468.5
Q32202.75
95-th percentile2790.15
Maximum2937
Range2937
Interquartile range (IQR)1468.5

Descriptive statistics

Standard deviation848.27187
Coefficient of variation (CV)0.57764513
Kurtosis-1.2
Mean1468.5
Median Absolute Deviation (MAD)734.5
Skewness0
Sum4314453
Variance719565.17
MonotonicityStrictly increasing
2023-01-06T00:05:57.105102image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
1951 1
 
< 0.1%
1953 1
 
< 0.1%
1954 1
 
< 0.1%
1955 1
 
< 0.1%
1956 1
 
< 0.1%
1957 1
 
< 0.1%
1958 1
 
< 0.1%
1959 1
 
< 0.1%
1960 1
 
< 0.1%
Other values (2928) 2928
99.7%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
2937 1
< 0.1%
2936 1
< 0.1%
2935 1
< 0.1%
2934 1
< 0.1%
2933 1
< 0.1%
2932 1
< 0.1%
2931 1
< 0.1%
2930 1
< 0.1%
2929 1
< 0.1%
2928 1
< 0.1%

Country
Categorical

Distinct193
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
Afghanistan
 
16
Peru
 
16
Nicaragua
 
16
Niger
 
16
Nigeria
 
16
Other values (188)
2858 

Length

Max length52
Median length34
Mean length10.041184
Min length4

Characters and Unicode

Total characters29501
Distinct characters56
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.3%

Sample

1st rowAfghanistan
2nd rowAfghanistan
3rd rowAfghanistan
4th rowAfghanistan
5th rowAfghanistan

Common Values

ValueCountFrequency (%)
Afghanistan 16
 
0.5%
Peru 16
 
0.5%
Nicaragua 16
 
0.5%
Niger 16
 
0.5%
Nigeria 16
 
0.5%
Norway 16
 
0.5%
Oman 16
 
0.5%
Pakistan 16
 
0.5%
Panama 16
 
0.5%
Papua New Guinea 16
 
0.5%
Other values (183) 2778
94.6%

Length

2023-01-06T00:05:57.217997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
republic 192
 
4.5%
of 192
 
4.5%
and 97
 
2.3%
united 64
 
1.5%
democratic 48
 
1.1%
the 48
 
1.1%
guinea 48
 
1.1%
saint 33
 
0.8%
ireland 32
 
0.7%
congo 32
 
0.7%
Other values (223) 3502
81.7%

Most occurring characters

ValueCountFrequency (%)
a 4190
 
14.2%
i 2535
 
8.6%
e 2178
 
7.4%
n 2104
 
7.1%
o 1638
 
5.6%
r 1635
 
5.5%
1350
 
4.6%
u 1126
 
3.8%
l 1110
 
3.8%
t 1107
 
3.8%
Other values (46) 10528
35.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23976
81.3%
Uppercase Letter 3967
 
13.4%
Space Separator 1350
 
4.6%
Open Punctuation 64
 
0.2%
Close Punctuation 64
 
0.2%
Other Punctuation 48
 
0.2%
Dash Punctuation 32
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4190
17.5%
i 2535
10.6%
e 2178
 
9.1%
n 2104
 
8.8%
o 1638
 
6.8%
r 1635
 
6.8%
u 1126
 
4.7%
l 1110
 
4.6%
t 1107
 
4.6%
d 867
 
3.6%
Other values (17) 5486
22.9%
Uppercase Letter
ValueCountFrequency (%)
S 466
 
11.7%
B 336
 
8.5%
C 289
 
7.3%
M 275
 
6.9%
A 256
 
6.5%
G 240
 
6.0%
R 240
 
6.0%
T 209
 
5.3%
I 194
 
4.9%
P 193
 
4.9%
Other values (14) 1269
32.0%
Space Separator
ValueCountFrequency (%)
1350
100.0%
Open Punctuation
ValueCountFrequency (%)
( 64
100.0%
Close Punctuation
ValueCountFrequency (%)
) 64
100.0%
Other Punctuation
ValueCountFrequency (%)
' 48
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27943
94.7%
Common 1558
 
5.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4190
15.0%
i 2535
 
9.1%
e 2178
 
7.8%
n 2104
 
7.5%
o 1638
 
5.9%
r 1635
 
5.9%
u 1126
 
4.0%
l 1110
 
4.0%
t 1107
 
4.0%
d 867
 
3.1%
Other values (41) 9453
33.8%
Common
ValueCountFrequency (%)
1350
86.6%
( 64
 
4.1%
) 64
 
4.1%
' 48
 
3.1%
- 32
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29485
99.9%
None 16
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4190
 
14.2%
i 2535
 
8.6%
e 2178
 
7.4%
n 2104
 
7.1%
o 1638
 
5.6%
r 1635
 
5.5%
1350
 
4.6%
u 1126
 
3.8%
l 1110
 
3.8%
t 1107
 
3.8%
Other values (45) 10512
35.7%
None
ValueCountFrequency (%)
ô 16
100.0%

Year
Real number (ℝ)

Distinct16
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.5187
Minimum2000
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:05:57.303221image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2000
Q12004
median2008
Q32012
95-th percentile2015
Maximum2015
Range15
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.6138409
Coefficient of variation (CV)0.0022982804
Kurtosis-1.2137217
Mean2007.5187
Median Absolute Deviation (MAD)4
Skewness-0.0064090274
Sum5898090
Variance21.287528
MonotonicityNot monotonic
2023-01-06T00:05:57.382538image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2013 193
 
6.6%
2015 183
 
6.2%
2014 183
 
6.2%
2012 183
 
6.2%
2011 183
 
6.2%
2010 183
 
6.2%
2009 183
 
6.2%
2008 183
 
6.2%
2007 183
 
6.2%
2006 183
 
6.2%
Other values (6) 1098
37.4%
ValueCountFrequency (%)
2000 183
6.2%
2001 183
6.2%
2002 183
6.2%
2003 183
6.2%
2004 183
6.2%
2005 183
6.2%
2006 183
6.2%
2007 183
6.2%
2008 183
6.2%
2009 183
6.2%
ValueCountFrequency (%)
2015 183
6.2%
2014 183
6.2%
2013 193
6.6%
2012 183
6.2%
2011 183
6.2%
2010 183
6.2%
2009 183
6.2%
2008 183
6.2%
2007 183
6.2%
2006 183
6.2%

Dev_Status
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
Developing
2426 
Developed
512 

Length

Max length10
Median length10
Mean length9.8257318
Min length9

Characters and Unicode

Total characters28868
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDeveloping
2nd rowDeveloping
3rd rowDeveloping
4th rowDeveloping
5th rowDeveloping

Common Values

ValueCountFrequency (%)
Developing 2426
82.6%
Developed 512
 
17.4%

Length

2023-01-06T00:05:57.473697image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-06T00:05:57.568248image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
developing 2426
82.6%
developed 512
 
17.4%

Most occurring characters

ValueCountFrequency (%)
e 6388
22.1%
D 2938
10.2%
v 2938
10.2%
l 2938
10.2%
o 2938
10.2%
p 2938
10.2%
i 2426
 
8.4%
n 2426
 
8.4%
g 2426
 
8.4%
d 512
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 25930
89.8%
Uppercase Letter 2938
 
10.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6388
24.6%
v 2938
11.3%
l 2938
11.3%
o 2938
11.3%
p 2938
11.3%
i 2426
 
9.4%
n 2426
 
9.4%
g 2426
 
9.4%
d 512
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
D 2938
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 28868
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6388
22.1%
D 2938
10.2%
v 2938
10.2%
l 2938
10.2%
o 2938
10.2%
p 2938
10.2%
i 2426
 
8.4%
n 2426
 
8.4%
g 2426
 
8.4%
d 512
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28868
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6388
22.1%
D 2938
10.2%
v 2938
10.2%
l 2938
10.2%
o 2938
10.2%
p 2938
10.2%
i 2426
 
8.4%
n 2426
 
8.4%
g 2426
 
8.4%
d 512
 
1.8%

Life_Expectancy
Real number (ℝ)

Distinct362
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.195643
Minimum36.3
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:05:57.651940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum36.3
5-th percentile51.385
Q163.025
median72
Q375.6
95-th percentile82
Maximum89
Range52.7
Interquartile range (IQR)12.575

Descriptive statistics

Standard deviation9.5366541
Coefficient of variation (CV)0.1378216
Kurtosis-0.24391734
Mean69.195643
Median Absolute Deviation (MAD)5.8
Skewness-0.63545466
Sum203296.8
Variance90.947771
MonotonicityNot monotonic
2023-01-06T00:05:57.763764image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73 45
 
1.5%
75 33
 
1.1%
78 31
 
1.1%
73.6 28
 
1.0%
73.9 25
 
0.9%
76 25
 
0.9%
81 25
 
0.9%
74.5 24
 
0.8%
74.7 24
 
0.8%
73.5 23
 
0.8%
Other values (352) 2655
90.4%
ValueCountFrequency (%)
36.3 1
< 0.1%
39 1
< 0.1%
41 1
< 0.1%
41.5 1
< 0.1%
42.3 1
< 0.1%
43.1 1
< 0.1%
43.3 1
< 0.1%
43.5 1
< 0.1%
43.8 1
< 0.1%
44 1
< 0.1%
ValueCountFrequency (%)
89 11
0.4%
88 10
0.3%
87 9
0.3%
86 15
0.5%
85 12
0.4%
84 11
0.4%
83.7 1
 
< 0.1%
83.5 2
 
0.1%
83.4 1
 
< 0.1%
83.3 1
 
< 0.1%

Adult_Mortality
Real number (ℝ)

Distinct425
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.81654
Minimum1
Maximum723
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:05:57.872236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q174
median144
Q3228
95-th percentile411
Maximum723
Range722
Interquartile range (IQR)154

Descriptive statistics

Standard deviation124.43375
Coefficient of variation (CV)0.75498337
Kurtosis1.7264088
Mean164.81654
Median Absolute Deviation (MAD)76
Skewness1.1701818
Sum484231
Variance15483.758
MonotonicityNot monotonic
2023-01-06T00:05:57.981226image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 34
 
1.2%
14 30
 
1.0%
16 29
 
1.0%
138 25
 
0.9%
11 25
 
0.9%
19 24
 
0.8%
144 22
 
0.7%
13 21
 
0.7%
17 21
 
0.7%
15 21
 
0.7%
Other values (415) 2686
91.4%
ValueCountFrequency (%)
1 12
0.4%
2 8
 
0.3%
3 6
 
0.2%
4 4
 
0.1%
5 2
 
0.1%
6 13
0.4%
7 16
0.5%
8 14
0.5%
9 12
0.4%
11 25
0.9%
ValueCountFrequency (%)
723 1
< 0.1%
717 1
< 0.1%
715 1
< 0.1%
699 1
< 0.1%
693 1
< 0.1%
686 1
< 0.1%
682 1
< 0.1%
679 1
< 0.1%
675 1
< 0.1%
666 1
< 0.1%

Infant_Deaths
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct209
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.303948
Minimum0
Maximum1800
Zeros848
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:05:58.121412image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q322
95-th percentile94.15
Maximum1800
Range1800
Interquartile range (IQR)22

Descriptive statistics

Standard deviation117.9265
Coefficient of variation (CV)3.8914567
Kurtosis116.04276
Mean30.303948
Median Absolute Deviation (MAD)3
Skewness9.786963
Sum89033
Variance13906.66
MonotonicityNot monotonic
2023-01-06T00:05:58.409199image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 848
28.9%
1 342
 
11.6%
2 203
 
6.9%
3 175
 
6.0%
4 96
 
3.3%
8 57
 
1.9%
7 53
 
1.8%
9 48
 
1.6%
10 48
 
1.6%
6 46
 
1.6%
Other values (199) 1022
34.8%
ValueCountFrequency (%)
0 848
28.9%
1 342
11.6%
2 203
 
6.9%
3 175
 
6.0%
4 96
 
3.3%
5 44
 
1.5%
6 46
 
1.6%
7 53
 
1.8%
8 57
 
1.9%
9 48
 
1.6%
ValueCountFrequency (%)
1800 2
0.1%
1700 2
0.1%
1600 1
< 0.1%
1500 2
0.1%
1400 1
< 0.1%
1300 2
0.1%
1200 1
< 0.1%
1100 2
0.1%
1000 1
< 0.1%
957 1
< 0.1%

Alcohol
Real number (ℝ)

Distinct1076
Distinct (%)36.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6072703
Minimum0.01
Maximum17.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:05:58.536745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.01
Q10.88
median3.765
Q37.665
95-th percentile11.963
Maximum17.87
Range17.86
Interquartile range (IQR)6.785

Descriptive statistics

Standard deviation4.044785
Coefficient of variation (CV)0.87791354
Kurtosis-0.80317058
Mean4.6072703
Median Absolute Deviation (MAD)3.265
Skewness0.58626679
Sum13536.16
Variance16.360285
MonotonicityNot monotonic
2023-01-06T00:05:58.655398image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 302
 
10.3%
7.3 21
 
0.7%
0.03 16
 
0.5%
0.02 14
 
0.5%
0.04 13
 
0.4%
0.09 13
 
0.4%
1.18 11
 
0.4%
0.06 11
 
0.4%
0.49 10
 
0.3%
0.17 10
 
0.3%
Other values (1066) 2517
85.7%
ValueCountFrequency (%)
0.01 302
10.3%
0.02 14
 
0.5%
0.03 16
 
0.5%
0.04 13
 
0.4%
0.05 10
 
0.3%
0.06 11
 
0.4%
0.07 5
 
0.2%
0.08 10
 
0.3%
0.09 13
 
0.4%
0.1 8
 
0.3%
ValueCountFrequency (%)
17.87 1
< 0.1%
17.31 1
< 0.1%
16.99 1
< 0.1%
16.58 1
< 0.1%
16.35 1
< 0.1%
15.52 1
< 0.1%
15.19 1
< 0.1%
15.14 1
< 0.1%
15.07 1
< 0.1%
15.04 2
0.1%

Percent_Expenditure
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2328
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean738.2513
Minimum0
Maximum19479.912
Zeros611
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:05:58.774911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.6853426
median64.912906
Q3441.53414
95-th percentile4506.6385
Maximum19479.912
Range19479.912
Interquartile range (IQR)436.8488

Descriptive statistics

Standard deviation1987.9149
Coefficient of variation (CV)2.6927347
Kurtosis26.573387
Mean738.2513
Median Absolute Deviation (MAD)64.912906
Skewness4.6520513
Sum2168982.3
Variance3951805.5
MonotonicityNot monotonic
2023-01-06T00:05:58.883668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 611
 
20.8%
71.27962362 1
 
< 0.1%
3.304039899 1
 
< 0.1%
218.5716179 1
 
< 0.1%
36.81621175 1
 
< 0.1%
2.542436908 1
 
< 0.1%
2.092343893 1
 
< 0.1%
22.35595448 1
 
< 0.1%
15.25518816 1
 
< 0.1%
31.50243237 1
 
< 0.1%
Other values (2318) 2318
78.9%
ValueCountFrequency (%)
0 611
20.8%
0.09987219 1
 
< 0.1%
0.108055973 1
 
< 0.1%
0.27564826 1
 
< 0.1%
0.328418056 1
 
< 0.1%
0.358651421 1
 
< 0.1%
0.388253772 1
 
< 0.1%
0.397228764 1
 
< 0.1%
0.442802404 1
 
< 0.1%
0.5305728 1
 
< 0.1%
ValueCountFrequency (%)
19479.91161 1
< 0.1%
19099.04506 1
< 0.1%
18961.3486 1
< 0.1%
18822.86732 1
< 0.1%
18379.32974 1
< 0.1%
17028.52798 1
< 0.1%
16255.16198 1
< 0.1%
15515.75234 1
< 0.1%
15345.4907 1
< 0.1%
15268.06445 1
< 0.1%

HepB
Real number (ℝ)

Distinct87
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.683799
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:05:59.002079image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q166
median89
Q396
95-th percentile99
Maximum99
Range98
Interquartile range (IQR)30

Descriptive statistics

Standard deviation28.851806
Coefficient of variation (CV)0.38121509
Kurtosis0.5971042
Mean75.683799
Median Absolute Deviation (MAD)9
Skewness-1.377775
Sum222359
Variance832.42671
MonotonicityNot monotonic
2023-01-06T00:05:59.110914image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 256
 
8.7%
98 243
 
8.3%
96 171
 
5.8%
95 162
 
5.5%
97 155
 
5.3%
93 133
 
4.5%
94 128
 
4.4%
92 108
 
3.7%
89 80
 
2.7%
91 75
 
2.6%
Other values (77) 1427
48.6%
ValueCountFrequency (%)
1 2
 
0.1%
2 32
1.1%
4 6
 
0.2%
5 19
 
0.6%
6 21
 
0.7%
7 43
1.5%
8 59
2.0%
9 75
2.6%
11 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
99 256
8.7%
98 243
8.3%
97 155
5.3%
96 171
5.8%
95 162
5.5%
94 128
4.4%
93 133
4.5%
92 108
3.7%
91 75
 
2.6%
89 80
 
2.7%

Measles
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct958
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2419.5922
Minimum0
Maximum212183
Zeros983
Zeros (%)33.5%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:05:59.223040image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median17
Q3360.25
95-th percentile9985.55
Maximum212183
Range212183
Interquartile range (IQR)360.25

Descriptive statistics

Standard deviation11467.272
Coefficient of variation (CV)4.7393409
Kurtosis114.8599
Mean2419.5922
Median Absolute Deviation (MAD)17
Skewness9.4413319
Sum7108762
Variance1.3149834 × 108
MonotonicityNot monotonic
2023-01-06T00:05:59.326156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 983
33.5%
1 104
 
3.5%
2 68
 
2.3%
3 44
 
1.5%
4 33
 
1.1%
6 29
 
1.0%
7 28
 
1.0%
5 25
 
0.9%
8 24
 
0.8%
9 22
 
0.7%
Other values (948) 1578
53.7%
ValueCountFrequency (%)
0 983
33.5%
1 104
 
3.5%
2 68
 
2.3%
3 44
 
1.5%
4 33
 
1.1%
5 25
 
0.9%
6 29
 
1.0%
7 28
 
1.0%
8 24
 
0.8%
9 22
 
0.7%
ValueCountFrequency (%)
212183 1
< 0.1%
182485 1
< 0.1%
168107 1
< 0.1%
141258 1
< 0.1%
133802 1
< 0.1%
131441 1
< 0.1%
124219 1
< 0.1%
118712 1
< 0.1%
110927 1
< 0.1%
109023 1
< 0.1%

BMI
Real number (ℝ)

Distinct608
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.02015
Minimum1
Maximum87.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:05:59.435031image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.785
Q119
median43
Q356.1
95-th percentile64.715
Maximum87.3
Range86.3
Interquartile range (IQR)37.1

Descriptive statistics

Standard deviation20.175077
Coefficient of variation (CV)0.53064169
Kurtosis-1.3096342
Mean38.02015
Median Absolute Deviation (MAD)16.6
Skewness-0.20244667
Sum111703.2
Variance407.03372
MonotonicityNot monotonic
2023-01-06T00:05:59.540917image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.1 22
 
0.7%
4.1 20
 
0.7%
58.5 18
 
0.6%
55.8 16
 
0.5%
57 16
 
0.5%
54.2 15
 
0.5%
59.9 15
 
0.5%
59.3 14
 
0.5%
52.8 13
 
0.4%
59.4 13
 
0.4%
Other values (598) 2776
94.5%
ValueCountFrequency (%)
1 1
 
< 0.1%
1.4 2
 
0.1%
1.8 1
 
< 0.1%
1.9 1
 
< 0.1%
2 1
 
< 0.1%
2.1 11
0.4%
2.2 9
0.3%
2.3 6
0.2%
2.4 5
0.2%
2.5 8
0.3%
ValueCountFrequency (%)
87.3 1
< 0.1%
83.3 1
< 0.1%
82.8 1
< 0.1%
81.6 1
< 0.1%
79.3 1
< 0.1%
77.6 1
< 0.1%
77.3 1
< 0.1%
77.1 1
< 0.1%
76.7 1
< 0.1%
76.2 1
< 0.1%

lt5_Deaths
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct252
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.035739
Minimum0
Maximum2500
Zeros785
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:05:59.648612image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q328
95-th percentile138
Maximum2500
Range2500
Interquartile range (IQR)28

Descriptive statistics

Standard deviation160.44555
Coefficient of variation (CV)3.8168842
Kurtosis109.7528
Mean42.035739
Median Absolute Deviation (MAD)4
Skewness9.4950647
Sum123501
Variance25742.774
MonotonicityNot monotonic
2023-01-06T00:05:59.779283image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 785
26.7%
1 361
 
12.3%
2 163
 
5.5%
4 161
 
5.5%
3 129
 
4.4%
12 53
 
1.8%
8 49
 
1.7%
6 48
 
1.6%
10 47
 
1.6%
5 44
 
1.5%
Other values (242) 1098
37.4%
ValueCountFrequency (%)
0 785
26.7%
1 361
12.3%
2 163
 
5.5%
3 129
 
4.4%
4 161
 
5.5%
5 44
 
1.5%
6 48
 
1.6%
7 30
 
1.0%
8 49
 
1.7%
9 40
 
1.4%
ValueCountFrequency (%)
2500 1
< 0.1%
2400 1
< 0.1%
2300 1
< 0.1%
2200 1
< 0.1%
2100 1
< 0.1%
2000 2
0.1%
1900 1
< 0.1%
1800 1
< 0.1%
1700 1
< 0.1%
1600 1
< 0.1%

Polio
Real number (ℝ)

Distinct73
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.307692
Minimum3
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:05:59.912147image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile9
Q177
median93
Q397
95-th percentile99
Maximum99
Range96
Interquartile range (IQR)20

Descriptive statistics

Standard deviation23.636677
Coefficient of variation (CV)0.28717458
Kurtosis3.6137881
Mean82.307692
Median Absolute Deviation (MAD)6
Skewness-2.0664036
Sum241820
Variance558.69249
MonotonicityNot monotonic
2023-01-06T00:06:00.046774image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 376
 
12.8%
98 255
 
8.7%
96 207
 
7.0%
97 205
 
7.0%
95 180
 
6.1%
94 159
 
5.4%
93 120
 
4.1%
92 96
 
3.3%
91 88
 
3.0%
9 77
 
2.6%
Other values (63) 1175
40.0%
ValueCountFrequency (%)
3 7
 
0.2%
4 11
 
0.4%
5 8
 
0.3%
6 11
 
0.4%
7 24
 
0.8%
8 40
1.4%
9 77
2.6%
17 1
 
< 0.1%
23 1
 
< 0.1%
24 2
 
0.1%
ValueCountFrequency (%)
99 376
12.8%
98 255
8.7%
97 205
7.0%
96 207
7.0%
95 180
6.1%
94 159
5.4%
93 120
 
4.1%
92 96
 
3.3%
91 88
 
3.0%
89 56
 
1.9%

Total_Expenditure
Real number (ℝ)

Distinct818
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.905211
Minimum0.37
Maximum17.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:06:00.172925image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.37
5-th percentile1.9685
Q14.26
median5.71
Q37.44
95-th percentile9.7415
Maximum17.6
Range17.23
Interquartile range (IQR)3.18

Descriptive statistics

Standard deviation2.48462
Coefficient of variation (CV)0.42075041
Kurtosis1.3511628
Mean5.905211
Median Absolute Deviation (MAD)1.55
Skewness0.67536079
Sum17349.51
Variance6.1733364
MonotonicityNot monotonic
2023-01-06T00:06:00.279034image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.56 24
 
0.8%
6.31 23
 
0.8%
4.6 16
 
0.5%
6.7 15
 
0.5%
2.77 15
 
0.5%
5.6 12
 
0.4%
3.4 12
 
0.4%
5.9 11
 
0.4%
4.36 11
 
0.4%
5.25 11
 
0.4%
Other values (808) 2788
94.9%
ValueCountFrequency (%)
0.37 1
 
< 0.1%
0.65 1
 
< 0.1%
0.74 1
 
< 0.1%
0.76 1
 
< 0.1%
0.92 1
 
< 0.1%
1.1 3
0.1%
1.12 3
0.1%
1.15 2
0.1%
1.17 2
0.1%
1.18 3
0.1%
ValueCountFrequency (%)
17.6 1
< 0.1%
17.24 2
0.1%
17.2 2
0.1%
17.14 1
< 0.1%
17 1
< 0.1%
16.9 1
< 0.1%
16.61 2
0.1%
16.2 1
< 0.1%
15.6 1
< 0.1%
15.57 1
< 0.1%

Diphtheria
Real number (ℝ)

Distinct81
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.075221
Minimum2
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:06:00.402427image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9
Q178
median93
Q397
95-th percentile99
Maximum99
Range97
Interquartile range (IQR)19

Descriptive statistics

Standard deviation23.917022
Coefficient of variation (CV)0.29140369
Kurtosis3.3990882
Mean82.075221
Median Absolute Deviation (MAD)6
Skewness-2.0395318
Sum241137
Variance572.02396
MonotonicityNot monotonic
2023-01-06T00:06:00.513193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 350
 
11.9%
98 254
 
8.6%
97 205
 
7.0%
96 201
 
6.8%
95 200
 
6.8%
94 149
 
5.1%
93 120
 
4.1%
92 100
 
3.4%
91 91
 
3.1%
89 76
 
2.6%
Other values (71) 1192
40.6%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 4
 
0.1%
4 12
 
0.4%
5 10
 
0.3%
6 16
 
0.5%
7 21
 
0.7%
8 39
1.3%
9 73
2.5%
16 1
 
< 0.1%
19 1
 
< 0.1%
ValueCountFrequency (%)
99 350
11.9%
98 254
8.6%
97 205
7.0%
96 201
6.8%
95 200
6.8%
94 149
5.1%
93 120
 
4.1%
92 100
 
3.4%
91 91
 
3.1%
89 76
 
2.6%

HIV/AIDS
Real number (ℝ)

Distinct200
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7421035
Minimum0.1
Maximum50.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:06:00.620858image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.1
median0.1
Q30.8
95-th percentile8.515
Maximum50.6
Range50.5
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation5.0777845
Coefficient of variation (CV)2.9147434
Kurtosis34.892008
Mean1.7421035
Median Absolute Deviation (MAD)0
Skewness5.396112
Sum5118.3
Variance25.783896
MonotonicityNot monotonic
2023-01-06T00:06:00.873332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 1781
60.6%
0.2 124
 
4.2%
0.3 115
 
3.9%
0.4 69
 
2.3%
0.5 42
 
1.4%
0.6 35
 
1.2%
0.9 32
 
1.1%
0.8 32
 
1.1%
0.7 29
 
1.0%
1.5 21
 
0.7%
Other values (190) 658
 
22.4%
ValueCountFrequency (%)
0.1 1781
60.6%
0.2 124
 
4.2%
0.3 115
 
3.9%
0.4 69
 
2.3%
0.5 42
 
1.4%
0.6 35
 
1.2%
0.7 29
 
1.0%
0.8 32
 
1.1%
0.9 32
 
1.1%
1 12
 
0.4%
ValueCountFrequency (%)
50.6 1
< 0.1%
50.3 1
< 0.1%
49.9 1
< 0.1%
49.1 1
< 0.1%
48.8 1
< 0.1%
46.4 1
< 0.1%
43.7 1
< 0.1%
43.5 1
< 0.1%
42.1 1
< 0.1%
40.7 1
< 0.1%

GDP
Real number (ℝ)

Distinct2490
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7475.5936
Minimum1.68135
Maximum119172.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:06:00.979673image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.68135
5-th percentile63.223863
Q1456.76653
median1680.8349
Q36454.0616
95-th percentile37813.234
Maximum119172.74
Range119171.06
Interquartile range (IQR)5997.2951

Descriptive statistics

Standard deviation13728.462
Coefficient of variation (CV)1.8364377
Kurtosis12.33742
Mean7475.5936
Median Absolute Deviation (MAD)1544.8364
Skewness3.1366648
Sum21963294
Variance1.8847067 × 108
MonotonicityNot monotonic
2023-01-06T00:06:01.093131image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1469.849149 49
 
1.7%
14672.8826 49
 
1.7%
3371.26869 49
 
1.7%
216.172747 34
 
1.2%
29986.2915 33
 
1.1%
18389.38433 33
 
1.1%
672.92113 18
 
0.6%
375.8528566 18
 
0.6%
4116.46693 17
 
0.6%
78.92744 17
 
0.6%
Other values (2480) 2621
89.2%
ValueCountFrequency (%)
1.68135 1
< 0.1%
3.685949 1
< 0.1%
4.6135745 1
< 0.1%
5.6687264 1
< 0.1%
8.376432 1
< 0.1%
11.147277 1
< 0.1%
11.33678 1
< 0.1%
11.553196 1
< 0.1%
11.631377 1
< 0.1%
12.1789279 1
< 0.1%
ValueCountFrequency (%)
119172.7418 1
< 0.1%
115761.577 1
< 0.1%
114293.8433 1
< 0.1%
113751.85 1
< 0.1%
89739.7117 1
< 0.1%
88564.82298 1
< 0.1%
87998.44468 1
< 0.1%
87646.75346 1
< 0.1%
86852.7119 1
< 0.1%
85948.746 1
< 0.1%

Population
Real number (ℝ)

Distinct2278
Distinct (%)77.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13168705
Minimum34
Maximum1.2938593 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:06:01.206866image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile8446
Q1136425.5
median1289898
Q37394105.5
95-th percentile49175848
Maximum1.2938593 × 109
Range1.2938593 × 109
Interquartile range (IQR)7257680

Descriptive statistics

Standard deviation56299844
Coefficient of variation (CV)4.2752757
Kurtosis319.83093
Mean13168705
Median Absolute Deviation (MAD)1271335
Skewness15.921272
Sum3.8689655 × 1010
Variance3.1696724 × 1015
MonotonicityNot monotonic
2023-01-06T00:06:01.322460image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49175848 65
 
2.2%
8446 49
 
1.7%
1289898 49
 
1.7%
18563 49
 
1.7%
943286 49
 
1.7%
82573 34
 
1.2%
8486 33
 
1.1%
4564297 33
 
1.1%
11719673 18
 
0.6%
542357 18
 
0.6%
Other values (2268) 2541
86.5%
ValueCountFrequency (%)
34 1
< 0.1%
36 1
< 0.1%
41 1
< 0.1%
43 1
< 0.1%
123 1
< 0.1%
135 1
< 0.1%
146 1
< 0.1%
286 1
< 0.1%
292 2
0.1%
297 1
< 0.1%
ValueCountFrequency (%)
1293859294 1
< 0.1%
1179681239 1
< 0.1%
1161977719 1
< 0.1%
1144118674 1
< 0.1%
1126135777 1
< 0.1%
258162113 1
< 0.1%
255131116 1
< 0.1%
248883232 1
< 0.1%
242524123 1
< 0.1%
236159276 1
< 0.1%

Thinness_1-19y
Real number (ℝ)

Distinct200
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9559564
Minimum0.1
Maximum27.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:06:01.435827image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.6
Q11.6
median3.4
Q37.3
95-th percentile14.7
Maximum27.7
Range27.6
Interquartile range (IQR)5.7

Descriptive statistics

Standard deviation4.5414034
Coefficient of variation (CV)0.91635257
Kurtosis3.3866731
Mean4.9559564
Median Absolute Deviation (MAD)2.4
Skewness1.6364319
Sum14560.6
Variance20.624345
MonotonicityNot monotonic
2023-01-06T00:06:01.541173image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 74
 
2.5%
1.9 65
 
2.2%
0.8 64
 
2.2%
0.7 63
 
2.1%
1.2 62
 
2.1%
2.1 61
 
2.1%
1.5 60
 
2.0%
2.2 58
 
2.0%
2 57
 
1.9%
0.9 57
 
1.9%
Other values (190) 2317
78.9%
ValueCountFrequency (%)
0.1 28
 
1.0%
0.2 41
1.4%
0.3 33
1.1%
0.4 5
 
0.2%
0.5 35
1.2%
0.6 41
1.4%
0.7 63
2.1%
0.8 64
2.2%
0.9 57
1.9%
1 74
2.5%
ValueCountFrequency (%)
27.7 1
 
< 0.1%
27.5 1
 
< 0.1%
27.4 1
 
< 0.1%
27.3 1
 
< 0.1%
27.2 2
0.1%
27.1 2
0.1%
27 3
0.1%
26.9 2
0.1%
26.8 2
0.1%
26.7 1
 
< 0.1%

Thinness_5-9y
Real number (ℝ)

Distinct207
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0025528
Minimum0.1
Maximum28.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:06:01.652124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q11.6
median3.4
Q37.3
95-th percentile15.015
Maximum28.6
Range28.5
Interquartile range (IQR)5.7

Descriptive statistics

Standard deviation4.6701535
Coefficient of variation (CV)0.93355408
Kurtosis3.7376175
Mean5.0025528
Median Absolute Deviation (MAD)2.4
Skewness1.713845
Sum14697.5
Variance21.810334
MonotonicityNot monotonic
2023-01-06T00:06:01.758512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9 69
 
2.3%
1.1 67
 
2.3%
0.5 63
 
2.1%
1.9 63
 
2.1%
1 62
 
2.1%
2.1 61
 
2.1%
1.3 59
 
2.0%
1.5 57
 
1.9%
1.7 55
 
1.9%
0.6 54
 
1.8%
Other values (197) 2328
79.2%
ValueCountFrequency (%)
0.1 37
1.3%
0.2 46
1.6%
0.3 26
 
0.9%
0.4 17
 
0.6%
0.5 63
2.1%
0.6 54
1.8%
0.7 46
1.6%
0.8 36
1.2%
0.9 69
2.3%
1 62
2.1%
ValueCountFrequency (%)
28.6 1
< 0.1%
28.5 1
< 0.1%
28.4 1
< 0.1%
28.3 1
< 0.1%
28.2 1
< 0.1%
28.1 1
< 0.1%
28 2
0.1%
27.9 1
< 0.1%
27.8 2
0.1%
27.7 1
< 0.1%

Income_Composition
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct625
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63141763
Minimum0
Maximum0.948
Zeros133
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:06:01.871595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.268
Q10.494
median0.684
Q30.791
95-th percentile0.89
Maximum0.948
Range0.948
Interquartile range (IQR)0.297

Descriptive statistics

Standard deviation0.21092014
Coefficient of variation (CV)0.33404221
Kurtosis1.3725658
Mean0.63141763
Median Absolute Deviation (MAD)0.124
Skewness-1.1672968
Sum1855.105
Variance0.044487305
MonotonicityNot monotonic
2023-01-06T00:06:01.979343image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 133
 
4.5%
0.798 56
 
1.9%
0.791 55
 
1.9%
0.808 37
 
1.3%
0.455 21
 
0.7%
0.268 19
 
0.6%
0.7 17
 
0.6%
0.739 13
 
0.4%
0.714 12
 
0.4%
0.636 12
 
0.4%
Other values (615) 2563
87.2%
ValueCountFrequency (%)
0 133
4.5%
0.253 1
 
< 0.1%
0.255 1
 
< 0.1%
0.261 1
 
< 0.1%
0.266 1
 
< 0.1%
0.268 19
 
0.6%
0.27 1
 
< 0.1%
0.276 1
 
< 0.1%
0.278 1
 
< 0.1%
0.279 1
 
< 0.1%
ValueCountFrequency (%)
0.948 1
 
< 0.1%
0.945 1
 
< 0.1%
0.942 1
 
< 0.1%
0.941 1
 
< 0.1%
0.939 1
 
< 0.1%
0.938 1
 
< 0.1%
0.937 1
 
< 0.1%
0.936 5
0.2%
0.934 2
 
0.1%
0.933 1
 
< 0.1%

Education
Real number (ℝ)

Distinct173
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.935671
Minimum0
Maximum20.7
Zeros29
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:06:02.091723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.6
Q110.1
median12.3
Q314.1
95-th percentile16.8
Maximum20.7
Range20.7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.3402021
Coefficient of variation (CV)0.27985039
Kurtosis0.92799932
Mean11.935671
Median Absolute Deviation (MAD)2
Skewness-0.62118269
Sum35067
Variance11.15695
MonotonicityNot monotonic
2023-01-06T00:06:02.193996image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.5 97
 
3.3%
11.8 81
 
2.8%
13 64
 
2.2%
12.9 58
 
2.0%
13.3 52
 
1.8%
12.8 46
 
1.6%
12.3 44
 
1.5%
12.6 43
 
1.5%
12.4 42
 
1.4%
11.9 41
 
1.4%
Other values (163) 2370
80.7%
ValueCountFrequency (%)
0 29
1.0%
2.8 1
 
< 0.1%
2.9 4
 
0.1%
3 1
 
< 0.1%
3.1 1
 
< 0.1%
3.3 1
 
< 0.1%
3.4 1
 
< 0.1%
3.5 3
 
0.1%
3.6 1
 
< 0.1%
3.7 2
 
0.1%
ValueCountFrequency (%)
20.7 1
 
< 0.1%
20.6 1
 
< 0.1%
20.5 1
 
< 0.1%
20.4 3
0.1%
20.3 4
0.1%
20.1 2
0.1%
19.8 1
 
< 0.1%
19.7 1
 
< 0.1%
19.5 3
0.1%
19.3 2
0.1%

Interactions

2023-01-06T00:05:54.188138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:09.712097image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:12.468162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:14.747540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:17.036583image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:19.211632image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:21.418215image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:23.699286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:25.929755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:28.111691image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:30.117350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:32.233477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:34.467245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:36.506789image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:38.814991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:41.005785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:43.152634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:45.403738image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:47.848751image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:50.054104image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:52.148489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:54.283848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:09.825437image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:12.587505image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:14.862273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:17.158061image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:19.316792image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:21.520826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:23.800657image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:26.030061image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:28.210115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:30.210146image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:32.344472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:34.563720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:36.606524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:38.909719image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:41.102884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:43.259720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:45.503401image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:47.947105image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:50.156067image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:52.242014image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:54.528296image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:09.941400image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:12.711841image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:15.013723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:17.266701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:19.423237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:21.627855image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:23.910567image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:26.129542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:28.310341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:30.318693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:32.444565image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:34.666666image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:36.713855image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:39.018732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:41.204405image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:43.510761image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:45.607346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:48.052882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:50.258100image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:52.346810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:54.622091image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:10.072760image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:12.826884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:15.120008image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:17.368392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:19.529338image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:21.728389image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:24.021124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:26.225464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:28.404292image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:30.425671image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:32.684319image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:34.764082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:36.812809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:39.114044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:41.302566image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:43.613995image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:45.708999image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:48.149150image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:50.356857image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:52.442050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:54.727525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:10.226669image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:12.943501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:15.230382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:17.473306image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:19.639637image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:21.828885image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:24.135090image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:26.324698image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:28.504375image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:30.531404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:32.789964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:34.865268image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:36.924877image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:39.214017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:41.406631image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:43.717539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:45.809633image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:48.247938image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:50.464235image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:52.549006image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:54.834474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:10.371251image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:13.060425image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:15.330027image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:17.585448image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:19.752959image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:22.078188image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:24.247524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:26.429037image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:28.604053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:30.644886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:32.893187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:34.968220image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:37.030644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:39.316493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:41.516552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:43.822646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:45.914346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:48.350920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-06T00:05:31.668284image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:33.874753image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:35.932539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:38.196435image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:40.276871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:42.560978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:44.813800image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:47.242768image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:49.486343image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:51.558126image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:53.606341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:55.878370image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:11.953334image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:14.169462image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:16.414246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:18.704898image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:20.903130image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:23.192440image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:25.401922image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:27.634460image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:29.650292image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:31.768476image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:33.974679image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:36.025969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:38.299915image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:40.377426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:42.668877image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:44.916490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:47.343825image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:49.581281image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:51.659886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:53.705447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:55.974145image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:12.057522image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:14.276585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:16.517601image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:18.809304image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:21.011918image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:23.294748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:25.507817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:27.731416image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:29.746207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:31.865355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:34.073863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:36.124444image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:38.404319image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:40.578121image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:42.777327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:45.018921image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:47.446319image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:49.684897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:51.760347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:53.804371image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:56.070458image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:12.155872image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:14.389041image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:16.611791image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:18.912222image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:21.114197image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:23.395804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:25.619337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:27.829071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:29.841437image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:31.957513image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:34.180927image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:36.218225image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:38.509239image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:40.709235image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:42.870320image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:45.115814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:47.546982image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:49.777380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:51.855401image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:53.909402image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:56.164705image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:12.259397image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:14.513890image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:16.714107image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:19.018862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:21.211585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:23.492691image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:25.728654image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:27.921953image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:29.938816image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:32.052224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:34.282467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:36.314619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:38.615340image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:40.818735image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:42.971458image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:45.216897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:47.649986image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:49.872204image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:51.957799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:54.008673image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:56.259787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:12.358232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:14.621181image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:16.803545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:19.116961image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:21.319001image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:23.601064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:25.828657image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:28.018813image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:30.030061image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:32.142155image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:34.375165image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:36.411439image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:38.715646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:40.913015image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:43.063587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:45.309725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:47.751963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:49.965000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:52.056103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:05:54.100869image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-01-06T00:06:02.300471image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Unnamed: 0YearLife_ExpectancyAdult_MortalityInfant_DeathsAlcoholPercent_ExpenditureHepBMeaslesBMIlt5_DeathsPolioTotal_ExpenditureDiphtheriaHIV/AIDSGDPPopulationThinness_1-19yThinness_5-9yIncome_CompositionEducationDev_Status
Unnamed: 01.000-0.004-0.0290.0270.040-0.048-0.101-0.0090.0580.0020.031-0.0100.015-0.0090.008-0.0490.0170.0260.041-0.022-0.0430.148
Year-0.0041.0000.153-0.053-0.052-0.092-0.0500.229-0.0950.147-0.0520.1160.0610.139-0.0560.1480.032-0.041-0.0400.1850.1860.000
Life_Expectancy-0.0290.1531.000-0.648-0.5940.4160.4290.363-0.2770.585-0.6120.5310.2810.541-0.7490.567-0.015-0.610-0.6200.8350.7910.627
Adult_Mortality0.027-0.053-0.6481.0000.390-0.200-0.297-0.2190.146-0.4010.403-0.320-0.175-0.3290.521-0.3460.0220.3990.413-0.533-0.4890.364
Infant_Deaths0.040-0.052-0.5940.3901.000-0.361-0.361-0.3360.573-0.4890.993-0.426-0.214-0.4230.487-0.4170.3180.4660.480-0.541-0.5950.065
Alcohol-0.048-0.0920.416-0.200-0.3611.0000.2630.108-0.1950.294-0.3600.2530.3320.263-0.1840.3810.015-0.434-0.4270.4940.5010.631
Percent_Expenditure-0.101-0.0500.429-0.297-0.3610.2631.0000.113-0.1530.282-0.3620.2130.1610.227-0.2550.5750.008-0.307-0.3090.4160.4660.448
HepB-0.0090.2290.363-0.219-0.3360.1080.1131.000-0.2590.266-0.3380.7190.1040.746-0.3720.229-0.067-0.116-0.1260.3540.3820.228
Measles0.058-0.095-0.2770.1460.573-0.195-0.153-0.2591.000-0.2760.574-0.255-0.175-0.2530.204-0.1690.2450.3120.325-0.203-0.2880.022
BMI0.0020.1470.585-0.401-0.4890.2940.2820.266-0.2761.000-0.5010.3320.2510.342-0.5240.386-0.019-0.574-0.5840.5840.5990.459
lt5_Deaths0.031-0.052-0.6120.4030.993-0.360-0.362-0.3380.574-0.5011.000-0.430-0.220-0.4260.512-0.4220.3110.4740.487-0.552-0.6060.060
Polio-0.0100.1160.531-0.320-0.4260.2530.2130.719-0.2550.332-0.4301.0000.1470.921-0.4830.348-0.068-0.233-0.2430.5220.5290.306
Total_Expenditure0.0150.0610.281-0.175-0.2140.3320.1610.104-0.1750.251-0.2200.1471.0000.153-0.1430.127-0.021-0.343-0.3570.2210.2670.404
Diphtheria-0.0090.1390.541-0.329-0.4230.2630.2270.746-0.2530.342-0.4260.9210.1531.000-0.4700.338-0.053-0.246-0.2540.5160.5320.315
HIV/AIDS0.008-0.056-0.7490.5210.487-0.184-0.255-0.3720.204-0.5240.512-0.483-0.143-0.4701.000-0.4330.0640.4830.471-0.631-0.6140.126
GDP-0.0490.1480.567-0.346-0.4170.3810.5750.229-0.1690.386-0.4220.3480.1270.338-0.4331.000-0.015-0.381-0.3880.6610.6040.429
Population0.0170.032-0.0150.0220.3180.0150.008-0.0670.245-0.0190.311-0.068-0.021-0.0530.064-0.0151.000-0.0070.0020.034-0.0180.067
Thinness_1-19y0.026-0.041-0.6100.3990.466-0.434-0.307-0.1160.312-0.5740.474-0.233-0.343-0.2460.483-0.381-0.0071.0000.949-0.574-0.5710.464
Thinness_5-9y0.041-0.040-0.6200.4130.480-0.427-0.309-0.1260.325-0.5840.487-0.243-0.357-0.2540.471-0.3880.0020.9491.000-0.575-0.5720.467
Income_Composition-0.0220.1850.835-0.533-0.5410.4940.4160.354-0.2030.584-0.5520.5220.2210.516-0.6310.6610.034-0.574-0.5751.0000.8810.686
Education-0.0430.1860.791-0.489-0.5950.5010.4660.382-0.2880.599-0.6060.5290.2670.532-0.6140.604-0.018-0.571-0.5720.8811.0000.602
Dev_Status0.1480.0000.6270.3640.0650.6310.4480.2280.0220.4590.0600.3060.4040.3150.1260.4290.0670.4640.4670.6860.6021.000

Missing values

2023-01-06T00:05:56.464739image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-06T00:05:56.755424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0CountryYearDev_StatusLife_ExpectancyAdult_MortalityInfant_DeathsAlcoholPercent_ExpenditureHepBMeaslesBMIlt5_DeathsPolioTotal_ExpenditureDiphtheriaHIV/AIDSGDPPopulationThinness_1-19yThinness_5-9yIncome_CompositionEducation
00Afghanistan2015Developing65.0263.0620.0171.27962465.0115419.1836.08.1665.00.1584.25921033736494.017.217.30.47910.1
11Afghanistan2014Developing59.9271.0640.0173.52358262.049218.68658.08.1862.00.1612.696514327582.017.517.50.47610.0
22Afghanistan2013Developing59.9268.0660.0173.21924364.043018.18962.08.1364.00.1631.74497631731688.017.717.70.4709.9
33Afghanistan2012Developing59.5272.0690.0178.18421567.0278717.69367.08.5267.00.1669.9590003696958.017.918.00.4639.8
44Afghanistan2011Developing59.2275.0710.017.09710968.0301317.29768.07.8768.00.163.5372312978599.018.218.20.4549.5
55Afghanistan2010Developing58.8279.0740.0179.67936766.0198916.710266.09.2066.00.1553.3289402883167.018.418.40.4489.2
66Afghanistan2009Developing58.6281.0770.0156.76221763.0286116.210663.09.4263.00.1445.893298284331.018.618.70.4348.9
77Afghanistan2008Developing58.1287.0800.0325.87392564.0159915.711064.08.3364.00.1373.3611162729431.018.818.90.4338.7
88Afghanistan2007Developing57.5295.0820.0210.91015663.0114115.211363.06.7363.00.1369.83579626616792.019.019.10.4158.4
99Afghanistan2006Developing57.3295.0840.0317.17151864.0199014.711658.07.4358.00.1272.5637702589345.019.219.30.4058.1
Unnamed: 0CountryYearDev_StatusLife_ExpectancyAdult_MortalityInfant_DeathsAlcoholPercent_ExpenditureHepBMeaslesBMIlt5_DeathsPolioTotal_ExpenditureDiphtheriaHIV/AIDSGDPPopulationThinness_1-19yThinness_5-9yIncome_CompositionEducation
29282928Zimbabwe2009Developing50.0587.0304.641.04002173.085329.04569.06.2673.018.165.8241211381599.07.57.40.4199.9
29292929Zimbabwe2008Developing48.2632.0303.5620.84342975.0028.64675.04.9675.020.5325.67857313558469.07.87.80.4219.7
29302930Zimbabwe2007Developing46.667.0293.8829.81456672.024228.24673.04.4773.023.7396.9982171332999.08.28.20.4149.6
29312931Zimbabwe2006Developing45.47.0284.5734.26216968.021227.94571.05.127.026.8414.79623213124267.08.68.60.4089.5
29322932Zimbabwe2005Developing44.6717.0284.148.71740965.042027.54369.06.4468.030.3444.765750129432.09.09.00.4069.3
29332933Zimbabwe2004Developing44.3723.0274.360.00000068.03127.14267.07.1365.033.6454.36665412777511.09.49.40.4079.2
29342934Zimbabwe2003Developing44.5715.0264.060.0000007.099826.7417.06.5268.036.7453.35115512633897.09.89.90.4189.5
29352935Zimbabwe2002Developing44.873.0254.430.00000073.030426.34073.06.5371.039.857.348340125525.01.21.30.42710.0
29362936Zimbabwe2001Developing45.3686.0251.720.00000076.052925.93976.06.1675.042.1548.58731212366165.01.61.70.4279.8
29372937Zimbabwe2000Developing46.0665.0241.680.00000079.0148325.53978.07.1078.043.5547.35887812222251.011.011.20.4349.8